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132 lines
5.2 KiB
132 lines
5.2 KiB
--- |
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comments: true |
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description: Comprehensive guide to setting up and using Ultralytics YOLO models in a Conda environment. Learn how to install the package, manage dependencies, and get started with object detection projects. |
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keywords: Ultralytics, YOLO, Conda, environment setup, object detection, package installation, deep learning, machine learning, guide |
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--- |
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# Conda Quickstart Guide for Ultralytics |
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<p align="center"> |
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<img width="800" src="https://user-images.githubusercontent.com/26833433/266324397-32119e21-8c86-43e5-a00e-79827d303d10.png" alt="Ultralytics Conda Package Visual"> |
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</p> |
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This guide provides a comprehensive introduction to setting up a Conda environment for your Ultralytics projects. Conda is an open-source package and environment management system that offers an excellent alternative to pip for installing packages and dependencies. Its isolated environments make it particularly well-suited for data science and machine learning endeavors. For more details, visit the Ultralytics Conda package on [Anaconda](https://anaconda.org/conda-forge/ultralytics) and check out the Ultralytics feedstock repository for package updates on [GitHub](https://github.com/conda-forge/ultralytics-feedstock/). |
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[](https://anaconda.org/conda-forge/ultralytics) [](https://anaconda.org/conda-forge/ultralytics) [](https://anaconda.org/conda-forge/ultralytics) [](https://anaconda.org/conda-forge/ultralytics) |
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## What You Will Learn |
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- Setting up a Conda environment |
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- Installing Ultralytics via Conda |
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- Initializing Ultralytics in your environment |
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- Using Ultralytics Docker images with Conda |
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--- |
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## Prerequisites |
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- You should have Anaconda or Miniconda installed on your system. If not, download and install it from [Anaconda](https://www.anaconda.com/) or [Miniconda](https://docs.conda.io/projects/miniconda/en/latest/). |
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--- |
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## Setting up a Conda Environment |
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First, let's create a new Conda environment. Open your terminal and run the following command: |
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```bash |
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conda create --name ultralytics-env python=3.8 -y |
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``` |
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Activate the new environment: |
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```bash |
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conda activate ultralytics-env |
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``` |
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--- |
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## Installing Ultralytics |
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You can install the Ultralytics package from the conda-forge channel. Execute the following command: |
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```bash |
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conda install -c conda-forge ultralytics |
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``` |
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### Note on CUDA Environment |
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If you're working in a CUDA-enabled environment, it's a good practice to install `ultralytics`, `pytorch`, and `pytorch-cuda` together to resolve any conflicts: |
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```bash |
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conda install -c pytorch -c nvidia -c conda-forge pytorch torchvision pytorch-cuda=11.8 ultralytics |
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``` |
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--- |
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## Using Ultralytics |
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With Ultralytics installed, you can now start using its robust features for object detection, instance segmentation, and more. For example, to predict an image, you can run: |
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```python |
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from ultralytics import YOLO |
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model = YOLO('yolov8n.pt') # initialize model |
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results = model('path/to/image.jpg') # perform inference |
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results.show() # display results |
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``` |
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--- |
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## Ultralytics Conda Docker Image |
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If you prefer using Docker, Ultralytics offers Docker images with a Conda environment included. You can pull these images from [DockerHub](https://hub.docker.com/r/ultralytics/ultralytics). |
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Pull the latest Ultralytics image: |
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```bash |
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# Set image name as a variable |
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t=ultralytics/ultralytics:latest-conda |
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# Pull the latest Ultralytics image from Docker Hub |
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sudo docker pull $t |
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``` |
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Run the image: |
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```bash |
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# Run the Ultralytics image in a container with GPU support |
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sudo docker run -it --ipc=host --gpus all $t # all GPUs |
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sudo docker run -it --ipc=host --gpus '"device=2,3"' $t # specify GPUs |
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``` |
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--- |
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Certainly, you can include the following section in your Conda guide to inform users about speeding up installation using `libmamba`: |
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--- |
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## Speeding Up Installation with Libmamba |
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If you're looking to [speed up the package installation](https://www.anaconda.com/blog/a-faster-conda-for-a-growing-community) process in Conda, you can opt to use `libmamba`, a fast, cross-platform, and dependency-aware package manager that serves as an alternative solver to Conda's default. |
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### How to Enable Libmamba |
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To enable `libmamba` as the solver for Conda, you can perform the following steps: |
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1. First, install the `conda-libmamba-solver` package. This can be skipped if your Conda version is 4.11 or above, as `libmamba` is included by default. |
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```bash |
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conda install conda-libmamba-solver |
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``` |
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2. Next, configure Conda to use `libmamba` as the solver: |
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```bash |
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conda config --set solver libmamba |
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``` |
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And that's it! Your Conda installation will now use `libmamba` as the solver, which should result in a faster package installation process. |
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--- |
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Congratulations! You have successfully set up a Conda environment, installed the Ultralytics package, and are now ready to explore its rich functionalities. Feel free to dive deeper into the [Ultralytics documentation](../index.md) for more advanced tutorials and examples.
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